Supplementary Table 3 Morphological attributes of
greenspace and adjacent reference built-up land. NDVI: normalized difference of
vegetation index; IMP: impervious surface fraction; SVF: sky view factor.
Values in parentheses are for built-up reference.
|
City |
ID |
Type |
NDVI |
IMP (%) |
Urban SVF |
Greenspace image |
Urban image |
|
San Lorenzo, USA |
1 |
Grass |
0.32
(0.09) |
19
(83) |
0.7 -
1 |
|
|
|
San Lorenzo, USA |
2 |
Tree-grass
Mixture |
0.37
(0.11) |
10
(96) |
0.7 -
1 |
|
|
|
San Lorenzo, USA |
3 |
Grass |
0.26
(0.11) |
42
(96) |
0.7 -
1 |
|
|
|
Los Angeles, USA |
4 |
Grass |
0.35
(0.09) |
9
(86) |
0.7 -
1 |
|
|
|
Los Angeles, USA |
5 |
Grass |
0.37
(0.12) |
12
(89) |
0.7 -
1 |
|
|
|
Los Angeles, USA |
6 |
Grass |
0.4
(0.12) |
19
(89) |
0.7 -
1 |
|
|
|
Los Angeles, USA |
7 |
Grass |
0.35
(0.12) |
23
(86) |
0.7 -
1 |
|
|
|
Los Angeles, USA |
8 |
Grass |
0.34
(0.10) |
17 (89) |
0.7 -
1 |
|
|
|
Los Angeles, USA |
9 |
Grass |
0.44
(0.08) |
19
(99) |
0.7 -
1 |
|
|
|
Los Angeles, USA |
10 |
Grass |
0.33
(0.09) |
34
(99) |
0.2 –
0.6 |
|
|
|
San Francisco, USA |
11 |
Tree
Canopy |
0.38
(0.10) |
10
(92) |
0.2 –
0.6 |
|
|
|
San Francisco, USA |
12 |
Grass |
0.36
(0.10) |
12 (92) |
0.2 –
0.6 |
|
|
|
San Francisco, USA |
13 |
Tree
Canopy |
0.35
(0.12) |
8
(92) |
0.2 –
0.6 |
|
|
|
San Francisco, USA |
14 |
Tree-grass
Mixture |
0.2
(0.13) |
21
(99) |
0.7 -
1 |
|
|
|
Provo, USA |
15 |
Grass |
0.44
(0.15) |
37
(96) |
0.2 –
0.6 |
|
|
|
Provo, USA |
16 |
Grass |
0.49
(0.15) |
27 (96) |
0.2 –
0.6 |
|
|
|
Provo, USA |
17 |
Grass |
0.6
(0.16) |
18
(96) |
0.2 –
0.6 |
|
|
|
Logan, USA |
18 |
Tree-grass
Mixture |
0.62
(0.19) |
10
(98) |
0.7 -
1 |
|
|
|
Logan, USA |
19 |
Tree-grass
Mixture |
0.64
(0.15) |
10
(92) |
0.2 –
0.6 |
|
|
|
Logan, USA |
20 |
Crop |
0.51
(0.24) |
6
(73) |
0.6 –
0.9 |
|
|
|
Logan, USA |
21 |
Tree
Canopy |
0.51
(0.14) |
25
(100) |
0.7 -
1 |
|
|
|
Logan, USA |
22 |
Grass |
0.41
(0.17) |
21
(99) |
0.7 -
1 |
|
|
|
Logan, USA |
23 |
Grass |
0.36
(0.17) |
9
(99) |
0.7 -
1 |
|
|
|
Logan, USA |
24 |
Crop |
0.5
(0.14) |
12
(98) |
0.7 -
1 |
|
|
|
Logan, USA |
25 |
Crop |
0.58 (0.16) |
6
(99) |
0.7 -
1 |
|
|
|
Logan, USA |
26 |
Grass |
0.59
(0.15) |
14
(73) |
0.7 -
1 |
|
|
|
Logan, USA |
27 |
Tree-grass
Mixture |
0.6
(0.14) |
31
(98) |
0.7 -
1 |
|
|
|
Logan, USA |
28 |
Crop |
0.37
(0.13) |
10
(98) |
0.7 -
1 |
|
|
|
London, UK |
29 |
Tree-grass
Mixture |
0.25
(0.16) |
22
(96) |
0.6 -
0.9 |
|
|
|
London, UK |
30 |
Tree-grass
Mixture |
0.39
(0.04) |
11
(100) |
0.3 -
0.6 |
|
|
|
London, UK |
31 |
Tree-grass
Mixture |
0.39
(0.08) |
14
(99) |
0.3 -
0.6 |
|
|
|
North Platte, USA |
32 |
Crop |
0.57
(0.16) |
3
(91) |
0.7 -
1 |
|
|
|
North Platte, USA |
33 |
Crop |
0.59
(0.34) |
1
(57) |
0.7 -
1 |
|
|
|
North Platte, USA |
34 |
Grass |
0.6
(0.25) |
1
(60) |
0.7 -
1 |
|
|
|
North Platte, USA |
35 |
Crop |
0.63
(0.31) |
2
(60) |
0.7 -
1 |
|
|
|
North Platte, USA |
36 |
Tree-grass
Mixture |
0.6
(0.27) |
3
(60) |
0.7 -
1 |
|
|
|
North Platte, USA |
37 |
Tree-grass
Mixture |
0.54 (0.27) |
9
(60) |
0.7 -
1 |
|
|
|
Anchorage, USA |
38 |
Tree-grass
Mixture |
0.47
(0.17) |
15
(66) |
0.7 -
1 |
|
|
|
Anchorage, USA |
39 |
Tree
Canopy |
0.26
(0.14) |
9
(87) |
0.7 -
1 |
|
|
|
Anchorage, USA |
40 |
Tree
Canopy |
0.73
(0.13) |
2
(78) |
0.7 -
1 |
|
|
|
Anchorage, USA |
41 |
Tree
Canopy |
0.44
(0.15) |
14
(80) |
0.7 -
1 |
|
|
|
Anchorage, USA |
42 |
Tree-grass
Mixture |
0.58
(0.16) |
9
(66) |
0.7 -
1 |
|
|
|
Anchorage, USA |
43 |
Tree-grass
Mixture |
0.41
(0.16) |
9
(66) |
0.7 -
1 |
|
|
|
Anchorage, USA |
44 |
Tree
Canopy |
0.57
(0.13) |
3
(78) |
0.7 -
1 |
|
|
|
Anchorage, USA |
45 |
Tree
Canopy |
0.5
(0.14) |
12
(66) |
0.7 -
1 |
|
|
|
Anchorage, USA |
46 |
Tree
Canopy |
0.51
(0.13) |
6
(78) |
0.7 -
1 |
|
|
|
Washington DC, USA |
47 |
Tree
Canopy |
0.74
(0.26) |
5
(90) |
0.5 -
0.8 |
|
|
|
Washington DC, USA |
48 |
Tree
Canopy |
0.5
(0.23) |
24
(90) |
0.5 -
0.8 |
|
|
|
Washington DC, USA |
49 |
Tree-grass
Mixture |
0.37
(0.23) |
56
(90) |
0.5 -
0.8 |
|
|
|
Washington DC, USA |
50 |
Tree-grass
Mixture |
0.37
(0.23) |
30
(90) |
0.5 -
0.8 |
|
|
|
Washington DC, USA |
51 |
Tree-grass
Mixture |
0.56
(0.24) |
18
(90) |
0.5 -
0.8 |
|
|
|
Washington DC, USA |
52 |
Grass |
0.5
(0.23) |
20
(90) |
0.5 -
0.8 |
|
|
|
Washington DC, USA |
53 |
Tree
Canopy |
0.66
(0.23) |
6
(90) |
0.5 -
0.8 |
|
|
|
New Haven, USA |
54 |
Tree
Canopy |
0.77
(0.34) |
6
(93) |
0.5 -
0.8 |
|
|
|
New Haven, USA |
55 |
Tree
Canopy |
0.73
(0.34) |
9
(94) |
0.3 -
0.6 |
|
|
|
New Haven, USA |
56 |
Tree-grass
Mixture |
0.56
(0.34) |
24
(93) |
0.5 -
0.8 |
|
|
|
New Haven, USA |
57 |
Tree
Canopy |
0.54
(0.40) |
7
(67) |
0.6 -
0.9 |
|
|
|
New Haven, USA |
58 |
Tree-grass
Mixture |
0.45
(0.28) |
35
(93) |
0.6 -
0.9 |
|
|
|
New Haven, USA |
59 |
Grass |
0.47
(0.25) |
31
(93) |
0.6 -
0.9 |
|
|
|
New Haven, USA |
60 |
Tree
Canopy |
0.55
(0.24) |
15
(93) |
0.6 -
0.9 |
|
|
|
New Haven, USA |
61 |
Tree
Canopy |
0.67
(0.41) |
10
(67) |
0.6 -
0.9 |
|
|
|
New Haven, USA |
62 |
Tree
Canopy |
0.81
(0.24) |
4
(85) |
0.7 -
1 |
|
|
|
New Haven, USA |
63 |
Tree-grass
Mixture |
0.62
(0.25) |
14
(93) |
0.6 -
0.9 |
|
|
|
New Haven, USA |
64 |
Tree
Canopy |
0.85
(0.25) |
3
(93) |
0.6 -
0.9 |
|
|
|
New Haven, USA |
65 |
Grass |
0.42
(0.25) |
22
(86) |
0.7 -
1 |
|
|
|
New Haven, USA |
66 |
Tree-grass
Mixture |
0.62
(0.34) |
4
(93) |
0.5 -
0.8 |
|
|
|
North Branford, USA |
67 |
Grass |
0.46
(0.16) |
0
(84) |
0.5 -
0.8 |
|
|
|
North Branford, USA |
68 |
Tree
Canopy |
0.57
(0.16) |
2
(84) |
0.5 -
0.8 |
|
|
|
North Branford, USA |
69 |
Crop |
0.47
(0.16) |
3
(84) |
0.5 -
0.8 |
|
|
|
North Branford, USA |
70 |
Tree
Canopy |
0.45
(0.16) |
10
(84) |
0.5 -
0.8 |
|
|
|
North Branford, USA |
71 |
Tree-grass
Mixture |
0.48
(0.16) |
10
(84) |
0.5 -
0.8 |
|
|
|
Nanjing, China |
72 |
Tree-grass
Mixture |
0.57
(0.35) |
46
(77) |
0.5 -
0.7 |
|
|
|
Nanjing, China |
73 |
Crop |
0.35
(0.33) |
21
(88) |
0.5 -
0.7 |
|
|
|
Nanjing, China |
74 |
Tree
Canopy |
0.49
(0.23) |
44
(93) |
0.2 -
0.4 |
|
|
|
Nanjing, China |
75 |
Tree-grass
Mixture |
0.53
(0.33) |
39
(88) |
0.5 -
0.7 |
|
|
|
Nanjing, China |
76 |
Tree
Canopy |
0.74
(0.45) |
4
(60) |
0.5 -
0.8 |
|
|
|
Nanjing, China |
77 |
Grass |
0.51
(0.33) |
38
(88) |
0.5 -
0.7 |
|
|
|
Nanjing, China |
78 |
Crop |
0.46
(0.33) |
36
(88) |
0.5 -
0.7 |
|
|
|
Nanjing, China |
79 |
Tree
Canopy |
0.62
(0.33) |
16
(88) |
0.5 -
0.7 |
|
|
|
Hangzhou, China |
80 |
Tree
Canopy |
0.5
(0.26) |
4
(57) |
0.7 -
1 |
|
|
|
Hangzhou, China |
81 |
Tree
Canopy |
0.40
(0.22) |
4
(57) |
0.7 -
1 |
|
|
|
Hangzhou, China |
82 |
Crop |
0.57
(0.30) |
7
(57) |
0.7 -
1 |
|
|
|
Hangzhou, China |
83 |
Crop |
0.58
(0.30) |
15
(57) |
0.7 -
1 |
|
|
|
Hangzhou, China |
84 |
Crop |
0.56
(0.30) |
13
(57) |
0.7 -
1 |
|
|
|
Hangzhou, China |
85 |
Crop |
0.46
(0.17) |
29
(82) |
0.5 -
0.7 |
|
|
|
Hangzhou, China |
86 |
Tree
Canopy |
0.68
(0.27) |
6
(81) |
0.2 -
0.4 |
|
|
|
Hangzhou, China |
87 |
Crop |
0.35
(0.24) |
12
(85) |
0.5 -
0.8 |
|
|
|
Hangzhou, China |
88 |
Tree
Canopy |
0.5
(0.24) |
6
(85) |
0.5 -
0.8 |
|
|
|
Hangzhou, China |
89 |
Grass |
0.4
(0.18) |
35
(82) |
0.5 -
0.7 |
|
|
|
Hangzhou, China |
90 |
Grass |
0.35
(0.20) |
27
(81) |
0.2 -
0.4 |
|
|
|
Hangzhou, China |
91 |
Grass |
0.41
(0.18) |
45
(81) |
0.2 -
0.4 |
|
|
|
Hangzhou, China |
92 |
Tree
Canopy |
0.40
(0.19) |
38
(83) |
0.5 -
0.7 |
|
|
|
Hangzhou, China |
93 |
Tree
Canopy |
0.41
(0.11) |
42
(92) |
0.3 -
0.6 |
|
|
|
Hangzhou, China |
94 |
Grass |
0.30
(0.19) |
39
(94) |
0.7 -
1 |
|
|
|
Hangzhou, China |
95 |
Crop |
0.32
(0.20) |
31
(81) |
0.2 -
0.4 |
|
|
|
Hangzhou, China |
96 |
Grass |
0.40
(0.19) |
28
(81) |
0.2 -
0.4 |
|
|
|
Hangzhou, China |
97 |
Tree-grass
Mixture |
0.39
(0.19) |
30
(81) |
0.2 -
0.4 |
|
|
|
Hangzhou, China |
98 |
Crop |
0.37
(0.20) |
31
(95) |
0.5 -
0.7 |
|
|
|
Hong Kong, China |
99 |
Tree
Canopy |
0.38
(0.08) |
53
(99) |
0.2 -
0.4 |
|
|
|
Hong Kong, China |
100 |
Tree
Canopy |
0.24
(0.07) |
55
(93) |
0.2 -
0.4 |
|
|
|
Hong Kong, China |
101 |
Tree
Canopy |
0.29
(0.08) |
25
(99) |
0.2 -
0.4 |
|
|
|
Hong Kong, China |
102 |
Tree-grass
Mixture |
0.37
(0.06) |
46
(99) |
0.2 -
0.4 |
|
|
|
Guangzhou, China |
103 |
Tree
Canopy |
0.57
(0.30) |
29
(72) |
0.5 -
0.7 |
|
|
|
Guangzhou, China |
104 |
Tree
Canopy |
0.49
(0.40) |
18
(71) |
0.5 -
0.7 |
|
|
|
Guangzhou, China |
105 |
Tree
Canopy |
0.40
(0.13) |
16
(100) |
0.6 -
0.9 |
|
|
|
Guangzhou, China |
106 |
Tree
Canopy |
0.35 (0.14) |
38
(100) |
0.6 -
0.9 |
|
|
|
Guangzhou, China |
107 |
Tree
Canopy |
0.32
(0.13) |
35
(100) |
0.6 -
0.9 |
|
|
|
Guangzhou, China |
108 |
Tree
Canopy |
0.34
(0.16) |
27
(99) |
0.6 -
0.9 |
|
|
|
Guangzhou, China |
109 |
Tree-grass
Mixture |
0.42
(0.29) |
50
(90) |
0.5 -
0.7 |
|
|
|
Guangzhou, China |
110 |
Tree
Canopy |
0.47
(0.18) |
19
(99) |
0.6 -
0.9 |
|
|
|
Guangzhou, China |
111 |
Crop |
0.37
(0.29) |
34
(83) |
0.2 -
0.4 |
|
|
|
Guangzhou, China |
112 |
Tree
Canopy |
0.56
(0.29) |
19
(83) |
0.2 -
0.4 |
|
|
|
Guangzhou, China |
113 |
Tree-grass
Mixture |
0.34
(0.29) |
25 (83) |
0.2 -
0.4 |
|
|
|
Guangzhou, China |
114 |
Tree-grass
Mixture |
0.47
(0.29) |
8
(83) |
0.2 -
0.4 |
|
|
Vegetation type is
based on visual inspection of Google Earth images. NDVI is warm season value
based on Sentinel-2 satellite observations. Cropland was identified using
FROM-GLC10 global landcover data 2017 (https://data-starcloud.pcl.ac.cn/resource/1). IMP is based on ESA Worldwide Land
Cover Mapping 2021 (https://esa-worldcover.org/en). SVF is the value of the dominant local climate
zone (LCZ) of the built-up neighborhood. LCZ classification data: https://www.wudapt.org. SVF values associated with LCZs from Stewart
(2012, ref46).